Deep Learning Algorithms for Automated Skin Cancer Detection: A Comparative Study

Skin cancer is one of the most common forms of cancer globally, with early detection playing a crucial role in improving patient outcomes. Traditional diagnostic methods rely heavily on dermatologists’ expertise, which can be subjective and time-consuming. Recent advancements in deep learning have shown remarkable potential in automating skin cancer detection from dermoscopic images. This study provides a comparative analysis of popular deep learning algorithms, including Convolutional Neural Networks (CNN), ResNet, DenseNet, and Vision Transformers (ViT), in terms of accuracy, sensitivity, specificity, and computational efficiency. Experimental results indicate that hybrid architectures such as CNN-Transformer combinations outperform conventional CNNs, offering a promising pathway for real-world clinical applications.